Feature subset selection for the multinomial logit model via mixed-integer optimization
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:1254-1263, 2019.
This paper is concerned with a feature subset selection problem for the multinomial logit (MNL) model. There are several convex approximation algorithms for this problem, but to date the only exact algorithms are those for the binomial logit model. In this paper, we propose an exact algorithm to solve the problem for the MNL model. Our algorithm is based on a mixed-integer optimization approach with an outer approximation method. We prove the convergence properties of the algorithm for more general models including generalized linear models for multiclass classification. We also propose approximation of loss functions to accelerate the algorithm computationally. Numerical experiments demonstrate that our exact and approximation algorithms achieve better generalization performance than does an L1-regularization method.